Method and means for data searching and language translation

ABSTRACT

The invention relates to data searching and translation. In particular, the invention relates to searching documents from the Internet or databases. Even further, the invention also relates to translating words in documents, WebPages, images or speech from one language to the next. A computer implemented method including at least one computer in accordance with the invention is characterised by the following steps: 
     receiving a search query including at least one search term, deriving at least one synonym for at least one search term, expanding the received search query with the at least one synonym, searching at least one document using the expanded search query, retrieving the search results obtained with the expanded query, ranking the search results based on context of occurrence of at least one search term. The best mode of the invention is considered to be an Internet search engine that delivers better search results.

TECHNICAL FIELD OF INVENTION

The invention relates to data searching and language translation. Inparticular, the invention relates to searching documents from theInternet or databases. Even further, the invention also relates totranslating words in documents, WebPages, images or speech from onelanguage to the next.

BACKGROUND

Searching from the Internet which spans the globe is a common experiencefor most office workers of today. Quite often though the searchexperience is frustrating, you either cannot find the documents you werelooking for, or the documents that might be relevant are in a languageyou cannot understand. This greatly hinders the productivity of theworld economy.

Internet search engines are known in the prior art, as well as machinetranslation software. One document that describes the situation in theprior art is WO 2009/002864 A2, which is cited here as reference. Inthis prior art a search query is inputted to a computer, for example ofthe form “How to ship a box”. In this prior art the terms in the queryare synonym expanded. For example the word ship can have synonyms e.g.,“boat” and “send”. The expanded synonym set is limited by removing theexpanded synonyms that are contextually irrelevant. That is, a synonymof a term in the search query is selected based on a context ofoccurrence of the term in the received search query in accordance withthis prior art. For example in the prior art, search results related tofishing trawlers are probably not relevant to shipping a box and thusthese search results are discarded.

SUMMARY

The invention under study is directed towards a system and a method foreffectively improving computerised search queries from data andcomputerised language translations from one language to the next.

The prior art has serious disadvantages. The inventor now asks: What ifthe person in the introduction is looking to ship a box of fish? Searchresults related to fishing trawlers might not be so irrelevant afterall.

The prior art methods for searching and translating data are deficientat least in the perspective that they can make an incorrect isolateddecision in the synonym expansion that leads to wrong results in theoverall search execution, as the search cannot recover to the desiredsearch results.

A further object of the invention is to solve the above problem andproduce search results and translations of improved quality.

In this application “synonym” of a word A is construed as a word B thathas similar meaning with word A, but is a different word. Spelling andtyping errors in a word are also regarded to change the word into asynonym in this application. I.e. “aple” could be a synonym to “apple”in accordance with the invention.

In this application “context” is construed as the overall and entirelogical, physical, and/or philosophical surroundings in which the wordis presented or might/should be understood.

“Synonym expansion” is construed as the expansion of the original searchand/or translation query into word sets having a similar meaning. Forexample a query “Big Aple” could be synonym expanded to:

“Big Aple”

“large apple”

“Big Apple”

“New York City”

“NYC”

“Natural language” means a past, present or future language spokenand/or written such as French, German, Esperanto, English or Chinese.

In one aspect of the invention the search query is expanded with searchterm synonyms. Search results are retrieved based on the expanded query.However, valid synonyms are not excluded because they merely appear tobe irrelevant. For example in the case of the query: “How to ship abox”, a trawler, albeit a fishing ship, would not be excluded. Now thiswill probably lead to a larger set of search results. The search resultsrelated to fishing trawlers are now still in the synonym expanded searchresult set.

In the invention the synonym expanded search results are then rankedbased on their contextual relevance. The contextual information that isused to perform the contextual ranking on the synonym expanded searchresults can be derived from various sources: global known parameters,data available from the computer of the user, recent browsing history ofweb pages, a statistical analysis of how users in the past have accessedsites listed in the synonym expanded search query, data open in anapplication on the computer of the user and any other information thatcharacterises the context in which the query is launched. For example,if the user has a browser window open with words “Cod” and/or “Salmon”,both generally known fish, the contextual relevance of the “fishingtrawler” is very strong, albeit it is not the closest synonym to ship.Therefore the search results with the fishing trawlers will not only beincluded in the search results, but will also rank high in the finallist of search results that is shown to the user.

In another use scenario of the invention, the search query “how to shipa box” comes from a computer known to reside in the Arabian Desert in,say Saudi Arabia. The same synonym expanded search result set isretrieved. Now, assume the user just visited FedEx™ website for 15seconds. Even though the search result that deals with fishing trawlersthat also deliver boxes of fish is in the retrieved results it will notrank high based on its contextual relevance. The websites of the SaudiArabian Post Office come up quite high based on their contextualrelevance, as well as any courier service that has a branch there.

Again, ranking is changed in favour of the fishing trawler search resultthat deals with boxes and deliveries, if the user has a document onSauvignon Blanc (the famous grape for white wine compatible with whitefish) open on his desktop.

In the invention no search results are lost by a deficient contextualdecision that performs a wrong decision on an isolated search term. Thisis a significant advantage of the invention.

Another embodiment of the invention deploys the same aforementionedinventive technique in translating documents from one language to thenext. A word to be translated into another language is analogous to asearch term in some embodiments of the invention. Let us assume thesentences to be translated into English are for example in Finnish:“Matti päätti käydä kylässä. Saavuttuaan Liisa tarjosi kahvit tuvassa.”Synonym expanded translations may be of the following type:

-   -   1) “Matti decided to visit. On arrival Liisa offered coffee in        the living room”    -   2) “Matti decided to drop by. On arrival Liisa offered coffee in        the house.”    -   3) “Matti decided to visit a village. On arrival Liisa offered        coffee in the living room”    -   4) “Matti determined to drop by in a village. On arrival Liisa        treated to coffee in a house”

And so on. Now the list of search results will be very long as differentpermutations and combinations of different expanded synonyms combined ina different way amount to a sizable list of search results. Inaccordance with the invention, a search result list with validlyexpanded synonyms that is broad is desirable. This quite extended listof synonym expanded search results will now be subject to context basedranking.

The context based ranking can be based on general statistical trendsobserved from other documents in the same language, words surroundingthe text to be translated, data available from the computer of the user,recent browsing history of WebPages, and any other information. Now, ifthe context somehow reveals that for example the events take place inHelsinki, the translations 1-2 are ranked higher, because generallyFinnish speakers would not refer to Helsinki as a village as it is thebiggest city and the capital city in the country. Likewise if thecontext reveals that Matti is unknown to Liisa, the translation 4 wouldrank contextually higher because “käydä kylässä” implies a social natureto the visit, and if Matti and Liisa do not know each other, it is morecontextually relevant to assume that they meet in a village and havecoffee that is merely offered by Liisa (e.g. in a house that is a café)rather than on Matti's personal visit in the living room of Liisa.

Thus in addition to determining the best matching document to a search,the invention can be used to produce the best matching translation. Aslong as outright wrong decisions in the synonym expansion are avoided,and there is enough contextual information available the outcome will bea considerably improved translation.

Some or all of the aforementioned advantages of the invention areaccrued with a browser and a search engine that translates the foreignpages using the synonym expansion-context ranking technique and displaysthe most relevant search results that are also retrieved with thesynonym expansion-context ranking technique. The invention thereforeguarantees the widest searches in scope, and the most logically relevantsearch results are displayed first.

A computer implemented method in accordance with the invention comprisesat least one computer and is characterised by the following steps:

-   -   receiving a search query comprising at least one search term,    -   deriving at least one synonym for at least one search term,    -   expanding the received search query with the at least one        synonym,    -   searching at least one document using the said expanded search        query,    -   retrieving the search results obtained with the said expanded        query,    -   ranking the said synonym expanded search results based on        context of occurrence of at least one search term.

An arrangement in accordance with the invention comprises at least onecomputer and is characterised in that:

-   -   a search query comprising at least one search term is arranged        to be received,    -   at least one synonym for at least one search term is arranged to        be derived,    -   the received search query is arranged to be expanded with the at        least one synonym,    -   at least one document is arranged to be searched using the said        expanded search query,    -   search results obtained with the said expanded query are        arranged to be retrieved,    -   the said synonym expanded search results are arranged to be        ranked based on context of occurrence of at least one search        term.

A memory unit in accordance with the invention comprises softwarecapable of executing the computer implemented search method and/oroperating the search arrangement described above.

A computer implemented method in accordance with the invention fortranslating text from one language to another language comprising atleast one computer is characterised by the following steps:

-   -   receiving a translation query comprising at least one term to be        translated,    -   deriving at least one synonym for at least one translation term,    -   expanding the received translation query with the at least one        synonym,    -   retrieving the translation results obtained with the said        expanded translation query,    -   ranking the said synonym expanded translation results based on        context of occurrence of at least one translation term.

A computer implemented arrangement in accordance with the invention fortranslating text from one language to another language comprising atleast one computer is characterised in that:

-   -   at least one translation query comprising at least one term to        be translated is arranged to be received,    -   at least one synonym for at least one translation term is        arranged to be derived,    -   the received translation query is arranged to be expanded with        the at least one synonym,    -   the translation results obtained with the said expanded        translation query are arranged to be retrieved,    -   the said synonym expanded translation results are arranged to be        ranked based on context of occurrence of at least one        translation term.

A memory unit in accordance with the invention comprises softwarecapable of executing the computer implemented translation method and/oroperating the translation arrangement described above.

A memory unit in accordance with the invention comprises software,characterised in that, the software is arranged to read data from adedicated part of a file system to extract contextual data for searchand/or translation.

A search engine software in accordance with the invention ischaracterised in that, the search engine is arranged to rank synonymexpanded search results based on context data derived from user'sInternet browser, operating system, file system, database softwareand/or applications.

Operating system software in accordance with the invention ischaracterised in that, the operating system is arranged to rank synonymexpanded search results based on context data derived from user'sInternet browser, operating system, file system, database softwareand/or applications.

A software application in accordance with the invention is characterisedin that, the software application is arranged to rank synonym expandedsearch results based on context data derived from user's Internetbrowser, operating system, file system, database software and/orapplications.

Database software in accordance with the invention is characterised inthat, the database software is arranged to rank synonym expanded searchresults based on context data derived from user's Internet browser,operating system, file system, database software and/or applications.

In addition and with reference to the aforementioned advantage accruingembodiments, the best mode of the invention is considered to be anInternet search engine that delivers better search results.

BRIEF DESCRIPTION OF THE DRAWINGS

In the following the invention will be described in greater detail withreference to exemplary embodiments in accordance with the accompanyingdrawings, in which

FIG. 1 demonstrates an embodiment of the inventive search method.

FIG. 2 demonstrates an embodiment 20 of the arrangement used to performthe searches and/or translations in accordance with the invention.

FIG. 3 demonstrates an embodiment 30 of the translation method inaccordance with the invention.

FIG. 4 demonstrates an embodiment 40 of the translation method usingaudiovisual input in accordance with the invention.

Some of the embodiments are described in the dependent claims.

DETAILED DESCRIPTION OF EMBODIMENTS

FIG. 1 shows an optimum computerised search method as a flow diagram inaccordance with the invention. In phase 100 the user inputs a searchquery. The input can take place by typing text for example with akeyboard or other computer peripheral, by speech when preferably speechrecognition is conducted by the computer to extract the query terms,and/or with an image when preferably pattern recognition is conducted bythe computer to extract the query terms. For example the user cantype:“how to ship a box” by a keyboard, or he can say “how to ship abox” and the speech recognition recognises the words and uses them assearch terms, and/or he can take an image with a camera or a mobilephone, of a ship with a box on board and the pattern recognitionsoftware recognises the ship and the box and extracts these as searchterms, and/or the user takes an image of a sign reading “how to ship abox” and the computer uses pattern and/or character recognition, such asObject Character Recognition (OCR) to identify the characters and thusthe words in the sign in accordance with the invention. It is also inaccordance with the invention that these input methods can be used in amix: the user can for example write “how to” and take an image of thebox on the ship and the written words “how to” and the patternrecognised words “ship” and “box” are combined to form the search queryin accordance with the invention. Likewise speech and text and image andspeech, or in fact all three forms of input, can be used in a mix inaccordance with the invention. The computer will arrange the terms inthe order in which they arrive or are recognised, or the user canspecify the order of the search terms, and/or the computer can suggestentire search queries to the user based on the recognised search termsin accordance with the invention.

In phase 110 the search query is expanded with search term synonyms.“how to” could be recognised as a phrase and expanded with a synonym “inwhat way”, the “ship” could be expanded with synonyms such as “boat” and“send”, the “box” could be expanded with a synonym “square” and“parcel”. In addition to words that have a synonymous meaning, thesynonym expansion will expand obvious writing versions of differentwords: for example “centre” (UK English) would be expanded to “center”(US English) in accordance with the invention. The inventive computingsystem can correct spelling errors such as “boks”, “sip”, “hou” thatmight have been caused by phonetic miss-spellings in the synonymexpansion, or separately before the synonym expansion. In addition theinventive computing system can correct spelling errors based ontypographical errors, for example in a QWERTY keyboard potential errorscould be “boz”, “shio”, “hoq” in accordance with the invention. Likewisethe computer system can expand different states of a noun, and/orcorrect words from different writing forms to standard forms. Forexample the Finnish language is notorious for having many states for anoun. A bay=lahti will be written landella=“on the bay” or “landessa=inthe bay”. In some embodiments of the invention the synonym expansionwill also expand the search terms to different linguistic states. In theexample above a search term “landella” would be expanded to also include“landessa”, “lahti” and possibly other states of the noun in accordancewith the invention.

In some embodiments of the invention some terms from the search querycan be omitted based on context of occurrence of a search term in orderto limit the search terms in accordance with practical limitations, suchas available computing power.

In phase 120 the search results are retrieved based on the expandedquery. The more expanded the query, the greater the number of searchresults. In some embodiments of the invention the expanded search queryis divided into several different search queries that are executed bythe same or different computers in parallel or in series, and the searchresults of each separate query are combined to form the overall searchresults for the expanded search query. In some embodiments of theinvention it is especially preferable that redundancies are eliminatedfrom the combined list of search results.

In phase 130 the search results are ranked contextually. It is importantin the invention that the ranking is based on the synonym expandedresults set. The synonym expansion increases the reach of the search anddelivers more search results, some of which may be or are probably evenundesirable to the user. The combined synergistic effect of thecontextual ranking imposed on the synonym expanded search result set isimportant in accordance with the invention. A search result that wouldhave been omitted by contextual rules used in the synonym expansion willstill be in the search result set in accordance with the invention, allthough it is likely to be proverbially buried underneath hundreds if notmillions of search results (depending on how expansive the synonymexpansion is) that also match the expanded search query. Now, when allthese results are ranked contextually, a search result that might havebeen on the millionth seed number in the synonym expanded search resultwill pop up to the first place, provided it has the best contextualmatch.

Contextual data, i.e. data for defining the context in which the synonymexpanded search results are ranked may be deduced from previous browsingbehaviour of the user, location of the user, time, date, nationality ofthe user, data on the computer of the user, general population browsingbehaviour, general statistics and frequency analysis of words, and datathat is open in an application on the computer of the user.

Using previous web pages of the user to define the context is preferableif the user has a consistent browsing behaviour, but many people don't.In the case of a person that checks the company intranet, and then readsa newspaper online, looks up an auction site or classifieds it mighttake a long browsing history to determine meaningful context data. Onepreferable embodiment of the invention is the use of an open applicationon the computer to define the context data used in ranking. For exampleif the user has a word processor open with a special term, say “NGC2547”, it will immediately suggest that a search term “star” actuallymeans a stellar object in space rather than people in Hollywood orBollywood. In this particular search the documents associated withcelebrities will be ranked to the last places in the context rankedsynonym expanded search result list. Documents dealing with stars inspace will rank higher, documents dealing with stars that have a NewGeneral Catalogue (NGC) number will rank higher still, whereas articleson stars in cluster NGC 2547 will rank higher still. An article on aHollywood actress claimed as a rising star, but with an explicitinterest in New General Catalogue numbers would of course be draggedhigher on the list than other celebrity articles, because that articlewould also have contextual merit from the point of view of the search.

In more detail, the context based ranking can be performed by severaldifferent methods and means in accordance with the invention. By contextof occurrence we mean the context in which at least one search termoccurs, i.e. the context of occurrence of at least one search term. Forexample when a user types a search query:“flights to London” in a searchengine or program, the context of occurrence of these search termscomprise data in other applications on the computer of the user when hemade the search query, and any data that is associated to the context inwhich the search terms now occur. If the user has an email message openwith “visit to London 1-3^(rd) March”, the “flights to London” searchterms occur in a context of the person going to London between 1-3^(rd)March, and therefore any last minute 3-day 2-night packages for 1-3^(rd)of March will be ranked contextually higher. The context of occurrencethus means the context data on the user's computer, and/or to which theuser's computer has access to as the search terms occur, i.e. areprovided to a search engine and/or program.

In one embodiment of the invention, the system picks words or numbersfrom the context, such as open applications, emails, messages, temporaryfolder, Desktop, calendar, clock, GPS chip, operating system, previouslybrowsed WebPages and/or the like. The picking of the words, numbers orother limiting factors to define the context can be done randomly, or bysome algorithm, or even by the user in some embodiments, in accordancewith the invention. The system now has the search terms that weresynonym expanded, typically a huge number of search results, and termsthat define the context.

Now the search results can be rearranged into an order where thedocument that is closest in similarity to the selected context terms isplaced first.

In one simple embodiment of the invention the received search resultsare simply ranked in the order of common hits with the context terms.Suppose that in our earlier example with the search query:“How to ship abox” the user has a webpage open that is titled “Shipping fish fast tosushi restaurants”, and suppose for example that the system knows we arein Vigo, Spain, probably the largest fishing port in the world. Thecontext terms “fish”, “fast”, “sushi” and “restaurant” are selected inaccordance with the invention. Now the document that tells about afishing trawler that sends sushi filets by direct delivery in boxes ofice after it docks in Vigo is probably proverbially buried on themillionth seed or similar in the search results. However, this documentwill now feature the highest frequency of the contextual terms, and willbe rearranged to first place in contextual ranking. In its simplestembodiment context based ranking of the invention is running a newsearch on the original search results based only on acquired contextwords.

It should be understood that different search terms and/or context termsmay have different weights, that may be assigned by the user, by thesystem, be updated from the network, or be pre-programmed into thecomputer in accordance with the invention.

It should also be understood that the context terms may be functional,rather than only plain words or numbers, for example context terms canbe acquired in functional form such as the date, time, location, andi.e. context terms can be functional or conditional beyond their plainmeaning.

In one embodiment contextual ranking is performed based on contextontologies. By ontology we mean: ontology ((computer science) a rigorousand exhaustive organization of some knowledge domain that is usuallyhierarchical and contains all the relevant entities and their relations.source: Wordnet).

A word may have an ontology in accordance with the invention, forexample the word “apple” will have an ontology of it being an example ofa fruit in general as well as being the genus to species varieties like“Calville Blanc” =the French culinary apple, or “Granny Smith (=theAustralian green apple with a strong taste) or Red Delicious (=thebright red apple that has a broad consumer appeal especially in the US),and the popular personal computer from California. In addition to theabove the word apple might have a conditional ontology, e.g. meaning NewYork City when having the prefix “Big” and being in capital letters “BigApple”.

Ontology of a word can be typically stored in a file that is read by atleast one search engine program. A search engine or the like can havedata files for all words in all common languages that define ontologiesof those words on one computer or distributed over a network ofcomputers. Alternatively, those files or a part of those files could bestored in the subscriber terminal, like the terminal computer the useris using, such as a PC or Apple Mac computer.

It should be noted that in accordance with the invention variableweights for different parts may exist within a word ontology. Withreflection to earlier example, “Granny Smith” could have a higherrelative weight in the “apple” ontology than “Calville Blanc” in someembodiments of the invention, because a priori one could assume moreusers searching for apples will be searching for Granny Smiths, becauseit is better known. The choice can of course be made the other wayaround providing a higher relative weight for “Calville Blanc” in someembodiments of the invention. As a general rule, the further theconnection is from the actual word itself, the lower the relativeweight. For the word “apple” the relative weight in the ontology for the“apple” itself will be 100% in some embodiments, whereas for “CalvilleBlanc” and “Granny Smith” it could be 1% and 2% respectively or viceversa in accordance with the invention.

As said the words that were chosen to define the context may haveontologies, and in fact in some embodiments those words with welldefined ontologies are typically chosen as context words over words thatdo not have an ontology, or have an ontology that is poorly defined orless used or the like.

In some embodiments the search results are searched with the ontologiesof the context terms and those search result documents that are mostsimilar to the ontologies of the context terms are prioritized in theranking, i.e. ranked before less relevant search result documents.

In some embodiments of the invention some or all selected context termsmay be synonym expanded too. The retrieved search results are thensearched again with the synonym expanded context terms, and the searchresults that are most similar or have the largest number of hits ofsynonym expanded context terms are ranked first in the contextualranking in some embodiments.

It should be understood that different search terms, context termsand/or ontologies may have different weights, which may be assigned bythe user, by the system, be updated from the network, or bepre-programmed into the computer in accordance with the invention.

It should also be understood that the context terms and/or ontologiesmay be functional, rather than only plain words or numbers, for examplecontext terms can be acquired in functional form such as the date, time,location, i.e. context terms can be functional beyond their plainmeaning. For example consider the context term “time: 12:00”. If thetime is +/−15 min from 12:00 in human terms time: 12:00 will verystrongly equate with “Now”. Quite clearly in this situation “now” wouldbe in the ontology of the context term time: 12:00, but onlyfunctionally and/or conditionally so, if it were 8.00 pm, “now” mightnot be in the ontology of the context term.

In more elaborate embodiments, a user or the user terminal may haveseveral contexts that have different ontologies such as for exampleEmail context, Browsing context, and/or Publication context.

For example an email context will contain other information besidescontent, such as the time, sender and/or receiver, their identity. Manytimes the actual content in the email is not remembered by people whowere corresponding; it is rather the sender and the time when it wassent and/or received, its importance classification or other parametersthat define the context ontology of the email context.

Browsing context can be read from the browser cache in some embodimentsof the invention. For example a webpage that was accessed at a certaintime should be interpreted in the context of other WebPages that wereaccessed at the same time, or with the same or similar query string. Thebrowser cache when read in this way can be used to determine thebrowsing context ontology in accordance with the invention.

Publication context on the other hand relates to what the user or userterminal decides to publish. Many users receive a lot of crap that isuninteresting to them, but people rarely post a webpage or sendelectronic mail or FTP files about issues that are completelyuninteresting to themselves, those issues may of course be uninterestingto others. The data that the user or the user terminal publishes on theInternet or to a select group in the form of messaging can be used todefine the ontology of the publication context.

When the context ontology is specified a user can assign a weight toparticular regions of the ontology, or to entire ontologies. For examplein the email context ontology all sent emails or terms or data thatappear in sent emails could be given a user assigned relative weight of100, when terms and/or data in received emails could be given a weightof 1. The user could set the aforementioned relative weights himself orthey could be pre-programmed or set from an Internet and/or networkserver by a third party. Quite clearly in the publication context theweights of some elements in the ontology could be a function ofpublication frequency for example. A webpage that the user is updatingall the time is likely to have a weight of 87 if it was updated 87 timesin comparison to a webpage that was only updated once (=relativeweight 1) for example, in some embodiments of the invention.

The different context and context ontologies may also have differentweights, which can be user assigned, assigned from a network serverand/or pre-programmed. An attorney that mainly uses email for his selfexpression will have an email context ontology that will have a higherrelevant weight to browsing or publication ontology. A media marketingperson will by contrast have a publication context ontology outweighingemail context ontology for example, as the most important things on hermind are the things she publishes by way of WebPages or press releasesfor example.

In its simplest embodiment, a certain context, take email context forexample, could have one or a collection of context terms that would bethe same all the time. If the user is a travel agent selling BritishAirways flights “British Airways” and “Eurotraveller” are prettyconstant context terms, because nearly all business is associated withbooking Eurotraveller economy seats to customers. If the ontology ofthese constant context terms would be constant all the time, this wouldin effect mean that the ontology of the context, i.e. email context inthis case would also be constant.

Of course having the same context terms or context ontology is notdesirable for versatile users, which account for the majority of allusers. In order to adapt to the changing wishes of users, the differentcontexts need to change too, and choose those context terms that appearto reflect the current user activity and intentions the best. If theuser for example has an email program open where he is writing an emailto be sent, it is a good bet for the system to acquire at least somecontext terms from the email the user is currently writing.

The aforementioned of course leads to the embodiment of dynamicalontology derivation for a context, when a context has different contextterms at different times or different situations, which is in accordancewith the invention. In some embodiments of the invention a number ofcontexts are being up to date as the user uses the computer system, andthe context with the best fit to user activity is preferably chosen allthe time, the context terms that best reflect user activity arepreferably chosen all the time, and the ontology of at least one contextterm and/or context is preferably up to date to reflect the useractivity.

It should be noted that different contexts may be combined in someembodiments to yield the context definition with different relativeweights. For example a user having an email message open and browsingthe Internet could be in a 70% email context 30% browsing context inaccordance with the invention. In this way different contexts may beused in combination in accordance with the invention.

It should be noted that different context terms within a contextontology may have different relative weights in accordance with theinvention. For example frequency of a term could add weight to a contextterm. If the user has an email message open that recites “flight” 5times, the context term “flight” should have a higher relative weightthan some less recited context term, on its own as an independentcontext term without a ontology, or in a context term ontology.

In summary different relative weights can be applied to search terms,context terms, contexts, search term ontologies, context termontologies, context ontologies and/or individual elements within anyontology and in this way any criteria for contextual ranking can beformed in accordance with the invention in any embodiments of theinvention.

Context based ranking based on context ontologies can be visualised withthe assistance of RDF (Resource Description Framework)—metadata graphs.One example of this is in FIG. 1 of “Desktop Search—How ContextualInformation Influences Search Results and Rankings” by Paiu and Nejdlwhich is cited here and incorporated as reference to this application.This RDF—metadata graph shows a context ontology which specifies contextmetadata from emails, files, web pages and publications with relationsamong them. The relations are described in “Activity Based Metadata forSemantic Desktop Search”—Proceedings of the 2^(nd) European Semantic WebConference, Heraklion, Greece, May 2005 which is also cited here asreference and incorporated into this application. The context ontologyelements and the relations between them can be used to search thesynonym expanded search results, and prioritise these search results bymeans of contextual ranking. The relations and metadata within theontology are typically derived from user activity in some embodiments,for example from when, to whom, and about what subject the user isemailing messages and with what attachment files. Typically thosesynonym expanded search results that rate highest in similarity to thecontext ontology that contains the metadata and/or relations are placedwith the best rank in the contextual ranking in accordance with theinvention.

Another embodiment with which to visualise context based ranking is withauthority transfer annotations using schema graphs which is alsoexplained in “Desktop Search—How Contextual Information InfluencesSearch Results and Rankings” by Paiu and Nejdl. Some metadata is moreimportant than other metadata, for example if the sender field containsthe boss of the employee, this would be more authorative metadata thanthe sender fields of the endless spam-emails most of us receive. In FIG.2 of the aforementioned document it is shown how authority istransferred within the context ontology, for example the attachment thatwas attached to an email that the boss sent is more authorative than ifthe file would have arrived by spam email. The more authorative anelement in the context ontology is, the higher relative weight it willhave in some embodiments of the invention. When the synonym expandedsearch results are ranked contextually by ranking them against thecontext ontology, those synonym expanded search results that havesimilarities to more important and/or authorative parts of the contextontology will be ranked first in accordance with the invention.

Furthermore, the document “Context-Aware Semantic Association Ranking byAleman-Meza, Halaschek, Arpinar and Sheth describes techniques forcontext based ranking in detail, and is included here as reference tothis application. It is in accordance with the invention that thecontextual ranking is based on semantic association ranking. In thisembodiment context ontology is defined as explained earlier. Now, thesynonym expanded search results are compared against the contextontology by seeing which search results are most closely semanticallyassociated with the context and/or context ontology. Semanticassociation between a context and a synonym expanded search result ismeasured by overall path weight. The overall path weigh W_(p)=+Userdefined weight+Context weight+Subsumption weight (class specializationin a path)+Trust weight (of the path between context & search result),as explained in Context-Aware Semantic Association Ranking byAleman-Meza, Halaschek, Arpinar and Sheth. It is in accordance with theinvention that the synonym expanded search results are contextuallyranked so, that those synonym expanded search results that are mostclosely semantically associated (and/or have the highest overall pathweight) with the specified context and/or context ontology are rankedfirst. Quite clearly it is in accordance with the invention that thesemantic association and/or the overall path length can be composed fromany permutation and/or combination of the aforementioned components. Forexample user defined weights could be left out in some embodiments ofthe invention.

As explained before contextual ranking of phase 130 may be performedwith different techniques. It is in accordance with the invention thatthese techniques can be combined and/or permuted in accordance with theinvention.

In phase 140 the contextually ranked synonym expanded search results aredisplayed to the user in the order of contextual relevance. Typicallythe said list is displayed in the monitor and the user can move todifferent positions in the list and select any search result for moredetailed viewing. In some embodiments some portion of the final searchresults might be discarded, for example the contextually least relevantsearch results. Discarding search results now is not as dangerous asbased on a contextual analysis of a search term earlier. The searchresult to be discarded has been considered vis-a-vis its objectivecontent of the substantive meaning of the search terms as well as itscontext in comparison to the context of the query as a whole or in partin some embodiments.

In fact it could be stated that as long as the synonym expansion isexpansive enough and the user provides enough information to define thecontext the invention will find the best match to the query of the userin the searched documents. It is another matter of course, if thedesired document is available, but by connecting the invention to theInternet users will find their desired information better than everbefore.

It should be noted that any features, phases or parts of the method 10can be freely permuted and combined with embodiments 20, 30, 40 inaccordance with the invention.

FIG. 2 shows an exemplary arrangement for conducting inventive searches.The embodiment 20 refers more particularly to Internet searches, but theinvention can of course be used with any data searches from any numberof documents or databases. The user will use the computer 210 with thekeyboard 220 and/or a peripheral like a mouse 200 and read and look atresults from the screen 230. If the search query is directed to at leastone database or document on the computer 210 the inventive search methodcan be operated “off-line” i.e. internally in the computer and thesearch results are shown on the screen 230. In some embodiments thesearch software needed to carry out the searches with the inventivemethod resides on the server 240 only and the documents on computer 210need to be searched with the inventive search method. In this embodimentthe server 240 searches the hard drives, memory, files or databases oncomputer 210.

In many embodiments part of the inventive system can be distributed toseveral servers, several client computers, and/or so that some parts ofthe system are distributed to at least one client computer, and someparts that are different or the same are distributed to a servercomputer.

The most typical use embodiment of the invention is normal Internetsearch. A search query is forwarded from the computer of the user to aserver of an Internet Search Engine. Exemplary prior art search enginesare currently known to operate by various names such as Google™, Bing™,Yahoo™, Lycos™ and the like. The server 240 receives the search queryand conducts the inventive Internet search. In some embodiments,especially those where the synonym expansion leads to a very largenumber of synonym expanded search queries, it is preferable for theserver to execute the search queries in parallel or in series asseparate search queries and collect the total list of the search resultsfrom the searches with the synonym expanded search queries. In thisembodiment it is preferable to eliminate redundancies from the totallist, (i.e. the sum list of synonym expanded search results) prior tothe contextual ranking, as the redundancy elimination leaves less searchresults to be ranked and saves processing resources.

The server 240 is typically arranged to search the Internet or databasesbut it may also search any files, documents, and/or data that it hasaccess to in accordance with the invention. It is in accordance with theinvention that there are more than one server 240 that share theprocessing of the search query, or the server 240 may relay the searchquery to another server in accordance with the invention.

It is in accordance with the invention that the computer 210, screen230, keyboard 220 and/or mouse 200 can be replaced by a mobile phone,laptop or any portable computing device, or in fact any device that canreceive a search query, irrespective of whether the computationalresources reside on that device and/or the server 240. It is inaccordance with the invention that the search queries can be inputted byspeech or images, in which case the computer could be replaced merely bya microphone, or a normal telephone wireless or wire line to receive thesearch query. In the case of image input the computer features, or isreplaced by a camera used to take the images.

In some embodiments it would be preferable to receive digital images asinput for the inventive search, but naturally film images can bedigitised and used by the inventive computerised search in accordancewith the invention.

All communications connections between any computers or devices whetherclient computers, dumb terminals or server devices can be arranged aswireless connections, such as radio/microwave/optical, for example GSM,CDMA, WIFI, free space optics (FSO), and/or fixed communicationsconnections such as data cable, POTS, and/or optical network e.g.SONET/SDH/Ethernet. Quite clearly the inventive method can be executedin an arrangement that comprises only one computer, several computers ora computer network.

The contextual data needed to perform the contextual ranking of thesynonym expanded search results can be obtained in a variety of ways:the server 240 may observe the previous searches made by the user, theInternet and/or database and/or document browsing behaviour of the userand deduce the context based on the occurrence of words in that documentin some embodiments.

The user may specify a context by text entry or multiple choice to thecomputer in some embodiments. There may be data on the computer 210 ofthe user that is used to define the context data in some embodiments.The data on the user's computer can be used to define context data in amultitude of ways: It is possible to use all data on the computer 210 todefine the context but this requires a great deal of data processing. Insome embodiments of the invention the user has a dedicated folder thatcontains documents that relate to the area of interest to the user. Thedocuments only in this folder are read to define the context data insome embodiments. For example, the user could have a scanned fishingpermit in his context folder on the computer. Whilst he would input “howto ship a box” the context would be “fishing” and the documents relatedto fishing trawlers shipping boxes of fish would rank high in the finalsearch results.

Indeed in some embodiments the contextual ranking based on a dedicatedcontext folder on the computer of the user can be used without synonymexpansion in accordance with the invention.

It should be noted that the inventive search method can be used also tosearch data stored in an application, for example an email applicationin some embodiments of the invention. The contextual ranking based on adedicated context folder on the computer of the user can be used withoutsynonym expansion to search for data in applications in accordance withthe invention in some embodiments. The contextual ranking based on adedicated context folder on the computer of the user or in the networkis inventive on its own right and may be used to rank search results,but also synonyms in accordance with the invention. For example thesynonyms of the synonym expansion could be ranked by the contextualranking based on contents in a dedicated context folder, with only thehighest ranking synonyms being accepted to at least one search query inaccordance with the invention.

Quite clearly any contextual ranking methods explained in associationwith phase 130 may be used in or by the arrangement 20.

It should be noted that any features, phases or parts of the arrangement20 can be freely permuted and combined with embodiments 10, 30, 40 inaccordance with the invention.

FIG. 3 shows an embodiment of the inventive search method deployed totranslation from one language to another language. Language translationis analogous to search in the sense that a corresponding word in anotherlanguage is being searched when a translation is performed. Now, inphase 300 a user, software, Internet browser, or search engine softwareenters a webpage with text in a foreign language. The process ofentering a webpage of a foreign language can be made invisible to theuser in some embodiments, i.e. the webpage entered is not shown to theuser requesting it in this embodiment.

In phase 310 text is recognised by the inventive computation system fromthe webpage that was entered in phase 300. Preferably text is recognisedin phase 310 in any language, even if it has a different character setto European Alphabets used here, such as Chinese, Japanese, Korean,Thai, Cyrillic, Greek, Indonesian, any Indian alphabet such as Urdu orHindi or the like. Also languages that are not used anymore, such asLatin or Egyptian hieroglyphs can be recognised in some embodiments inaccordance with the invention.

On some web pages textual data is not stored in the form of text, butrather the text is in an image, which image is on the webpage. It is inaccordance with the invention to recognise the text from the image databy character recognition, for example OCR (Object CharacterRecognition), or similar recognition algorithm.

In phase 320 the text in the foreign language is translated to synonymexpanded text sets in the preferred language of the user. For example ifthe webpage would have contained a sentence “How to ship a box” and theuser would use Finnish as his preferred language, be it native languageor best language or any language of choice the translated synonymexpanded sets could include for example:

“Kuinka randata laatikko laivalla”,

“Kuinka lahettaa laatikko”,

“Mina keinoin lahettaa paketti”, and so on.

Now, in some embodiments of the invention the inventive translationsystem defines a text set as the text between commas, a comma and a fullstop, between full stops, or a block of words of some length, e.g. 10words.

In phase 330 the synonym expanded text set results are ranked based oncontextual relevance. If there is only one text set on the webpage, thecontextually most relevant text set is shown to the user as thetranslation in his preferred or native language. In some embodiments thetranslation is shown in a different application and/or Internet browserwindow on the computer of the user. In some embodiments the Internetbrowser shows the translated text in the same place on the webpage, i.e.thereby producing a ‘virtual webpage’ in a different language thatotherwise looks the same as the original webpage, except that it is in adifferent language. The contextual data needed to perform the contextualranking can be obtained similarly to other embodiments explained in thisapplication.

If there is more than one text set on the webpage, in phase 340 thecombinations of translated text set results are ranked based on theircontextual relevance. The contextual relevance can be estimated forexample based on how well the different translated text sets fittogether, i.e. does the translation make sense in combination?

The contextual ranking of translations can be performed by any methodsdiscussed in association with phase 130. For example a sentence istranslated into several synonym expanded translation results. Thesynonym expanded translation results will then be compared in similarityto the other sentences in the text. Typically a paragraph of manmadetext will feature antecedents and the like that cause the same or verysimilar terms to be carried out all through the text. The translationresult that is contextually most similar to at least one surroundingsentence, or the words in a translation result that are most similar tosurrounding words, will be chosen as the translation in preferredembodiments of the invention. In its simplest embodiment a translatedword can be chosen based on its contextual similarity to only at leastone other single word.

Quite clearly translation results can be contextually ranked by any ofthe methods explained in association with phase 130 in accordance withthe invention. As the ranking has been achieved, typically thecontextually highest ranking translation will be offered to the user asthe final translation in accordance with the invention.

It should be understood that different search terms and/or context termsmay have different weights, that may be assigned by the user, by thesystem, be updated from the network, or be pre-programmed into thecomputer in accordance with the invention.

It should also be understood that the context terms may be functional,rather than only plain words or numbers, for example context terms canbe acquired in functional form such as the date, time, location, i.e.context terms can be functional or conditional beyond their plainmeaning.

Similarly to as explained in phase 130 translated words can be comparedagainst context words, context word ontologies and/or context ontologiesin accordance with the invention. In some embodiments of the inventiontranslated word ontologies may be compared against context words,context word ontologies, and/or context ontologies. Similarly toembodiments explained in phase 130 translated word groups and/orsentences can be compared against context words, context wordontologies, and/or context ontologies in accordance with the invention.Typically the translation result that has the greatest similarity withthe context words, context word ontologies, and/or context ontologieswill be ranked the highest in the contextual ranking and chosen as thetranslation in accordance with the invention.

It is also possible that semantic associations are used similarly asexplained in phase 130. The translation result that has the highestsemantic path weight to at least one context word and/or or contextwords is typically selected as the translation by the contextual rankingin some embodiments of the invention. For example, different grammarrules could be implemented by path weights: If the sentence has asubject followed by a verb which appears to be the predicate of thesentence, it is likely that at least one noun that follows will be theobject of the verb. The nouns that are in the state of an object willthus carry higher path weights in that sentence. Quite clearly grammarrules can be deployed by the other contextual ranking techniques inaccordance with the invention also. In one embodiment of the inventionbasic grammar rules are arranged to be programmed into the ontology ofwords.

Contextual relevance can also be based on data in the context folder.I.e. a Finnish user looking for fishing gear from typically Swedishshops that manufactures most of the desired fishing equipment can placefor example Finnish Fishing Permit documentation into the contextfolder, as an image, text or document file, for example. The InternetBrowser and/or the Search program will sense that the overall contextnow is fishing by analyzing the data in the context folder. As theFinnish user starts to browse the web pages in Swedish explainingproduct descriptions of lures, nets, rods etc. in Swedish the internetbrowser will translate the Swedish into Finnish and the Finnish customercan read the translated ‘virtual’ webpage from his browser.

The combination of text sets with the highest contextual rank isdisplayed to the user as the translation in phase 350 on the webpage, ina separate window, and/or on the webpage in the same place where theoriginal text was, i.e. producing a virtual webpage in the translatedlanguage.

Therefore an Internet browser and/or search engine that translates webpages to a defined language, and shows the foreign web pages in thedesired language is in accordance with the invention.

It is also in accordance with the invention that as the inventivetranslation software works on the web pages, the total body of web pagesin any preferred language will increase. Some of these web pages may beindexed or even stored by search engines in accordance with theinvention. These translated web pages can now of course be subjected tosearches, normal searches or searches with the inventive search methodsdiscussed earlier in accordance with the invention.

Quite clearly the invention can be used to translate any document fromone language to the next; the use of the invention is not restricted toweb browsing.

It should be noted that any features, phases or parts of the method 30can be freely permuted and combined with embodiments 10, 20, 40 inaccordance with the invention.

FIG. 4 displays the search-translation embodiment 40 in accordance withthe invention, which is better suited to different categories of input.In phase 400 a user speaks at least one word in a baseline language,which typically is his preferred language, native language or thelanguage he knows best. In fact, the baseline language can be anylanguage in accordance with the invention. In phase 410 speechrecognition identifies the spoken words and converts them to text. Insome embodiments speech can be supplemented and/or replaced by imagedata, from which (image data) text is extracted by pattern and/orcharacter recognition. In some embodiments image and speech data can bealso recognised in combination, for example a video camera will recordboth voice and images, and speech and/or sound recognition can be usedto identify words from the audio and pattern recognition and/orcharacter recognition can be used to recognise text in image format,words that have image equivalent, (image of a ball identified as=“ball”). In the aforementioned ways, and other ways, audiovisual datacan be distilled into text. This text can also be translated to anotherlanguage in accordance with the invention. For example a distilled textin natural language English can be translated to French natural languagein accordance with the invention.

In phase 420 the text extracted from audiovisual data is synonymexpanded in the desired target language as explained elsewhere in thisapplication. Different text sets are expanded with different synonymexpansions. Now, as the input data arrives from a variety of sources,all the text can be synonym expanded as one text set in accordance withthe invention, but sometimes it is more preferable to divide the inputtext into text sets. The text that is derived by speech recognitioncould be sorted separately, based on what voice speaks the words andother factors. In exemplary embodiments of the invention the computersystem can identify who does the speaking. The speech recognised wordswith a certain voice can be recognised in blocks of 10 words or more orless, or for example the word groups in between pauses can be recognisedas distinct text sets in accordance with the invention. Patternrecognised words from image data can be identified as individual wordsor word set that can be synonym expanded as a text set for example. Itis also in accordance with the invention to combine words extracted fromdifferent types of audiovisual data.

In fact, the textual extraction of words from audiovisual data bypattern/character recognition is an inventive embodiment in its ownright, without the synonym expansion or contextual ranking in accordancewith the invention.

However, it is possible and in accordance with the invention that arecognised pattern/character will have synonyms in accordance with theinvention, possibly many synonyms. For example a pattern shaped like a“C” could be the letter “C” in the European alphabet but it could alsobe a half part of a stadium or a velodrome in a picture, “halfvelodrome”, “half stadium” and “C” would be synonyms in this case. Thisis the translation from character based language to a natural languagein accordance with the invention. It is in accordance with the inventionthat the translation from audiovisual data may not use contextualranking or synonym expansion in some embodiments as it is inventive inits own right. It is also possible in accordance with the invention thatonly synonym expansion or contextual ranking are used individually inaccordance with the invention. However, in some embodiments it ispreferable to use both synonym expansion and contextual ranking, and insome embodiments even more preferably so that the contextual rankingwill only be conducted to the synonym expanded translation text setcandidate.

In phase 430 the translated text sets, or those sets that wereoriginally in the baseline language and did not need to be translated,are ranked based on contextual relevance.

The combinations of translated text sets are then ranked based oncontextual relevance in phase 440.

The combination with the highest contextual rank is reproduced to theuser as the translation and/or extraction of text. The reproduction cantake place by reproducing the words as voice from a loudspeaker, or asimages and/or footage on a screen and/or video that combines the two inthe baseline language of the user in phase 450.

In some embodiments of the invention it is possible not to use thetranslation feature between natural languages e.g. (translating Englishtraffic signs into English text then speech). Useful embodiment beingfor example a car camera and a computer that reads out traffic signs tothe driver as they are videoed and/or photographed. It is possible andin accordance with the invention for the user to operate only the textextraction feature, i.e. translating patterns, characters, image, voice,into textual words. In some embodiments of the invention the extractedtextual words that correspond to the data are searched by the synonymexpansion—contextual ranking inventive technique.

The invention therefore makes it possible to extract a textual narrativefrom audiovisual data in the Internet or in the surrounding world aroundus that is video recorded, and provides for the textual translation ofthat data.

One simple embodiment of the invention could be realised for exampleinto a portable device that translates English speech to a Frenchloudspeaker broadcast using the synonym expand—context rank technique.

However, the ability to extract data from different media types greatlyenhances especially the context based ranking. Say for example the useris with a mobile phone in a Finnish harbour. He just took a picture fromthe harbour. The pattern recognition will recognise words “sea” and“ship” from the image, which was incidentally saved to the contextfolder (that acts as the source of context data) of the mobile device.“How to ship a box” will now be translated into Finnish “Kuinka randatalaatikko laivalla”, instead of the other alternatives, because thecontext is clear, we are trying to get a box onto an actual ship to somedestination, rather than looking for the post office.

It is also in accordance with the invention to use a text document inthe context folder of a language speech converter in accordance with theinvention.

It should be noted that any features, phases or parts of the method 40can be freely permuted and combined with embodiments 10, 20, 30 inaccordance with the invention.

The invention has been explained above with reference to theaforementioned embodiments and several commercial and industrialadvantages have been demonstrated. The methods and arrangements of theinvention allow people to find relevant documents, based on theinventive synonym expansion-contextual ranking on that synonym expandedword set-technique. The same technique provides for more accuratetranslations from one natural language to another language from theInternet or documents, or from a combination of image language andspeech to a natural language, either in textual or spoken form.

The invention has been explained above with reference to theaforementioned embodiments. However, it is clear that the invention isnot only restricted to these embodiments, but comprises all possibleembodiments within the spirit and scope of the inventive thought and thefollowing patent claims.

REFERENCES

WO 2009/002864 A2, Reizler, Stefan, Vasserman, Alexander, I. “Machinetranslation for query expansion”.

“Desktop Search—How Contextual Information Influences Search Results andRankings” Raluca Paiu and Wolfgang Nejdl.

“Context—Aware Semantic Association Ranking” Aleman-Meza Boanerges,Halaschek Chris, Arpinar I. Budak and Sheth Amit.

“Activity Based Metadata for Semantic Desktop Search”—Proceedings of the2^(nd) European Semantic Web Conference, Heraklion, Greece, May 2005, P.Chirita, R. Gavriloaie, S. Ghita, W. Nejdl and R. Paiu.

1. A computer implemented method, characterised by the following steps:receiving a search query comprising at least one search term, derivingat least one synonym for at least one search term, expanding thereceived search query with the at least one synonym, searching at leastone document using at least one said expanded search query, retrievingthe search results obtained with at least one said expanded query,ranking the said synonym expanded search results based on context ofoccurrence of at least one search term.
 2. A computer implemented methodas claimed in claim 1, characterised in that, the context is derivedfrom data available from the user's previous searches, data on theuser's computer and/or general statistics.
 3. An arrangement comprisingat least one computer, characterised in that: a search query comprisingat least one search term is arranged to be received, at least onesynonym for at least one search term is arranged to be derived, thereceived search query is arranged to be expanded with the at least onesynonym, at least one document is arranged to be searched using at leastone said expanded search query, search results obtained with at leastone said expanded query are arranged to be retrieved, the said synonymexpanded search results are arranged to be ranked based on context ofoccurrence of at least one search term.
 4. A memory unit comprisingsoftware capable of executing the method of claim
 1. 5. A computerimplemented method for translating text from one language to anotherlanguage comprising at least one computer, characterised by thefollowing steps: receiving a translation query comprising at least oneterm to be translated, deriving at least one synonym for at least onetranslation term, expanding the received translation query with the atleast one synonym, retrieving the translation results obtained with atleast one said expanded translation query, ranking the said synonymexpanded translation results based on context of occurrence of at leastone translation term.
 6. A computer implemented method for translatingtext as claimed in claim 5, characterised in that, the translation queryis a sentence or a paragraph that is automatically read as input fortranslation or analysed from audiovisual data such as image and/orsound.
 7. A computer implemented method for translating text as claimedin claim 5, characterised in that, the context of occurrence is derivedfrom sentences and/or paragraphs that precede and/or follow the saidtranslation query.
 8. A computer implemented arrangement for translatingtext from one language to another language comprising at least onecomputer, characterised in that: at least one translation querycomprising at least one term to be translated is arranged to bereceived, at least one synonym for at least one translation term isarranged to be derived, the received translation query is arranged to beexpanded with the at least one synonym, the translation results obtainedwith at least one said expanded translation query are arranged to beretrieved, the said synonym expanded translation results are arranged tobe ranked based on context of occurrence of at least one translationterm.
 9. A memory unit comprising software capable of executing themethod of claim
 5. 10. A memory unit comprising software, characterisedin that, the software is arranged to read data from a dedicated part ofa file system to extract contextual data for search and/or translation.11. A search engine software, characterised in that, the search engineis arranged to rank synonym expanded search results based on contextdata derived from user's Internet browser, operating system, filesystem, database software and/or applications.
 12. Operating systemsoftware, characterised in that, the operating system is arranged torank synonym expanded search results based on context data derived fromuser's Internet browser, operating system, file system, databasesoftware and/or applications.
 13. A software application, characterisedin that, the software application is arranged to rank synonym expandedsearch results based on context data derived from user's Internetbrowser, operating system, file system, database software and/orapplications.
 14. Database software, characterised in that, the databasesoftware is arranged to rank synonym expanded search results based oncontext data derived from user's Internet browser, operating system,file system, database software and/or applications.